Ottawa F-65 Sand Data from Ana Maria Parra Bastidas
Bibliographic record
Abstract
The dataset contains detailed characterization data for Ottawa F-65 index properties and element test results. It provides a comprehensive experimental dataset used to describe the physical and mechanical behavior of this widely studied granular soil. This dataset is intended for use in research on soil liquefaction, constitutive modeling, cyclic soil response, and laboratory-based soil characterization. It supports calibration and validation of numerical models, comparative analysis of sand behavior under different loading histories, and benchmarking of constitutive models used in geotechnical earthquake engineering. The database includes scanning electron microscope (SEM) images, soil index properties as grain size distribution data, specific gravity measurements, maximum and minimum density values, and permeability characteristics. It contains element test data such as one-dimensional compression tests, monotonic direct simple shear (DSS), cyclic DSS tests, and cyclic pre-straining direct simple shear tests. As a reference, PhD Dissertation of Ana Maria Parra Bastidas is available in database. Together, these datasets provide a broad experimental basis for evaluating sand behavior under static and cyclic loading conditions. This dataset was originally published on May 17, 2016 on the NEEShub cyberinfrastructure (a predecessor of NHERI and DesignSafe) and later hosted in the DataCenterHub. Due to the discontinuation of both programs, the dataset was re-curated and is now publicly available through DesignSafe. Elements of a complete description may be missing or point to projects and events that preceded NHERI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".